Actionable companion · Feedback & scaffolding
Learning with agency: The Usability of Neurological Occupational Therapy Case Studies Generated by ChatGPT
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
Evidence and context
Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Abstract Background/Objectives: Case-based learning is essential in occupational therapy education, but developing standardized cases requires significant resources. This study explores the usability of AI-generated case studies using ChatGPT. Methods: A four-stage process was applied: (1) prompt development based on existing frameworks, (2) case generation ensuring diversity and relevance, (3) expert evaluation using a 5-point Likert scale, and (4) data analysis. Five neurological cases were generated and reviewed by ten experts. Results: Experts rated the cases highly in clinical realism (4.22/5), information comprehensiveness (4.56/5), and educational value (4.44/5). The AI-generated cases successfully provided structured occupational therapy scenarios, assessment results, and clinical questions. Conclusions: Five AI-generated occupational therapy cases were developed and reviewed by occupational therapy experts to evaluate their clinical realism, comprehensiveness, and educational value. While expert feedback was favorable, the effectiveness of these cases for student learning has not yet been empirically tested. Keywords: clinical simulation, educational technology, large language model, virtual patient
What kind of evidence is this?
Supporting AI learning science · Design requires full-text appraisal
Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.
Study methods · source passage
Prompt development: Reviewing previous studies to determine essential case elements, such as diagnosis, patient background, and occupational performance issues.
Limitations and uncertainty · source passage
Occupational therapists require rapid and effective decision-making skills to facilitate clients’ occupational recovery. This process begins with clinical reasoning and assessment of clients’ functional limitations, which are crucial for treatment planning [1]. Thus, occupational therapy students undergo practice-based education and licensing examinations to verify their practical competencies [2].
Design the system before the pilot
Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.
- Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
- Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
A 90-day district pilot
- Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
- Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
- Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.
The board decision brief
Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.
Attribution and reuse
Si-An Lee; Jin-Hyuck Park. The Usability of Neurological Occupational Therapy Case Studies Generated by ChatGPT. Healthcare (2025-06-04). https://doi.org/10.3390/healthcare13111341
Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.